Triple
T37822525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Busto Arsizio Film Festival |
E942958
|
entity |
| Predicate | countryOfOriginFilms |
P204410
|
FINISHED |
| Object | Italy |
E863
|
NE FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Italy | Statement: [Busto Arsizio Film Festival, countryOfOriginFilms, Italy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryOfOriginFilms Context triple: [Busto Arsizio Film Festival, countryOfOriginFilms, Italy]
-
A.
filmCountryOfOrigin
Indicates the country where a film was originally produced or created.
-
B.
characterInFilmCountryOfOrigin
Indicates that a character appearing in a film is associated with the country where that film originated or was produced.
-
C.
productionCountryOfWorkAppearsIn
Indicates that a country is the production country of a work in which a given entity appears.
-
D.
filmStudioCountry
Indicates the country in which a film studio is based or primarily operates.
-
E.
productionCountries
Indicates the countries where a work (such as a film or TV show) was produced or financed.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76ee987588190906506e759be5db3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037cae084081909004d77514c5f286 |
completed | May 12, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a40fb68665c819086d0959577c0e47f |
completed | June 28, 2026, 10:46 a.m. |
| PD | Predicate disambiguation | batch_6a037a1772e48190ba738c6d11b321e2 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c84ecbc81908232e5215355f43b |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:19 p.m.